The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Machine learning is a branch of artificial intelligence in which systems improve their performance on a task by learning patterns from data rather than following explicitly programmed rules. It is broadly divided into supervised learning for classification and regression from labeled examples, unsupervised learning for clustering and dimensionality reduction on unlabeled data, and reinforcement learning for learning optimal actions through trial-and-error interaction with an environment. Common algorithms include decision trees, support vector machines, ensemble methods such as random forests and gradient boosting, and neural networks, chosen based on data characteristics, interpretability needs, and computational constraints. Industry surveys suggest most organizations remain in experimentation or pilot phases with machine learning, with only about a third reporting they have begun scaling programs organization-wide. Machine learning underlies applications across nearly every domain, including predictive maintenance, credit scoring, medical diagnosis support, and recommendation systems. As an open-access machine learning journal (an ML journal), IJACSA publishes comparative studies and applied research spanning these algorithm families.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
In the current fiercely competitive landscape, an organization’s ability to succeed depends on its ability to leverage information technology to support personnel decisions that optimise the use of its people resources.…
Sentiment analysis is vital for understanding public opinion, but improving its performance is challenging due to the complexities of high-dimensional text data and diverse user-generated content. We propose a novel fram…
Cybercrimes originate in a variety of forms, and the majority of crimes involve credit cards. Despite various steps taken to prevent credit card fraud, it is crucial to alert customers to unusual attempts at fraudulent t…
Anticipating student performance in higher education is crucial for informed decision-making and the reduction of dropout rates. This study focuses on the intricate analysis of diverse educational datasets using machine…
Early claims in the life insurance sector can lead to significant financial losses if not properly managed. This paper experiments a number of feature selection such as values regrouping, over or undersampling, and encod…
This research paper delves into the intricate domain of cyberbullying detection on social media, addressing the pressing issue of online harassment and its implications. The study encompasses a comprehensive exploration…
Precisely calculating the cooling load is essential to improving the energy efficiency of cooling systems, as well as maximizing the performance of chillers and air conditioning controls. Machine learning (ML) has better…
PCOS is a common endocrine disorder that impacts women in their reproductive years characterized by irregular menstrual cycles, hyperandrogenism, and polycystic ovaries. Polycystic Ovary Syndrome (PCOS) presents signific…
The overarching objective of this study lies in the thorough evaluation of the effectiveness of K-nearest neighbors (KNN) models in the precise estimation of building cooling load consumption. This assessment holds signi…
Heart disease remains a global health concern, demanding early and accurate prediction for improved patient outcomes. Machine learning offers promising tools, but existing methods face accuracy, class imbalance, and over…
